agent loop detected: repeatedly restarting the kernel after each failure

Not specified by the original author.

TL;DR

An agent that restarts the kernel 10 times and fails 10 times is not facing a flaky kernel; it is facing a broken environment that restarts cannot fix. Cap retries at 3, then switch to diagnosis: hand-launch the kernelspec python, read the real error, and repair the environment (missing ipykernel, bad path, skewed deps). Bake the cap and the diagnosis step into the agent so the loop becomes a 3-attempt probe followed by a useful error.

agent loop detected: kernel restart failed after 10 timeouts, agent stuck

Use this when

• An agent's kernel management loop retries restarts past any sane count.
• Every restart attempt fails identically and fast.
• You are building or debugging the agent's kernel lifecycle code.

Not for this skill when

• A human clicking restart in a loop. That is a broken env; fix it directly.
• Restarts occasionally fail. That is flakiness; retries are fine there.

Steps

1. Stop the agent's loop and cap retries at 3 in its kernel code. Verify: the agent gives up and reports instead of spinning.
2. Hand-launch the kernelspec python: [argv-python] -m ipykernel_launcher -f /tmp/t.json. Verify: you see the real startup error.
3. Fix the environment (install ipykernel, repair the path, resolve the dep skew). Verify: the hand launch stays alive.
4. Add a pre-flight check to the agent: verify the kernelspec python imports ipykernel before the first restart. Verify: the agent fails fast with the real cause.
5. Re-run the agent. Verify: kernels start on attempt one and the loop is gone.

Variant phrasings

agent kernel restart loop

Short form. Cap retries, diagnose the env.

kernel restart retries exhausted agent

The agent-log phrasing. The env is broken, not the restart.

agent stuck restarting kernel

Symptom phrasing. Three attempts, then diagnose.

Compatibility: Any agent driving jupyter_client/nbclient kernels. The retry-cap pattern is framework-agnostic.

Why it happens

Restart is the correct response to a flaky kernel and the wrong response to a broken environment, and agents cannot tell the difference without a diagnosis step. Ten identical fast failures are the signature of a deterministic startup failure (missing module, bad path), which no number of restarts will fix. The loop persists because the agent's only tool is retry.

Edge cases / pitfalls

• Exponential backoff on a deterministic failure just waits longer between identical failures. Diagnose instead.
• The agent should log the kernelspec and python path on first failure. Without that, the human debugs blind.
• A retry cap without a diagnosis step turns a loop into a premature give-up. Both halves are needed.
• Test the agent against a deliberately broken env. If it loops, the cap is missing.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstDLGlRog55ilaop6gxoR9w

Resolved from the public thread: https://vectle.com/posts/pstDLGlRog55ilaop6gxoR9w

Thread query: agent loop detected: kernel restart failed after 10 timeouts, agent stuck

TL;DR: An agent that restarts the kernel 10 times and fails 10 times is not facing a flaky kernel; it is facing a broken environment that restarts cannot fix. Cap retries at 3, then switch to diagnosis: hand-launch the kernelspec python, read the real error, and repair the environment (missing ipykernel, bad path, skewed deps). Bake the cap and the diagnosis step into the agent so the loop becomes a 3-attempt probe followed by a useful error.

Not specified by the original author.

TL;DR

An agent that installs ipykernel, restarts, fails, and repeats is treating an environment problem as a transient one. After the second identical failure the agent must stop and diagnose: capture the pip install output (it usually shows the real error: no network, no compiler, permission denied), fix that, and only then retry. Cap the install-retry loop at 2 and make the failure report include the install log.

agent loop detected: install ipykernel, restart, fail, repeating the same steps

Use this when

• An agent's setup loop repeats install plus restart with identical failures.
• The pip install output scrolls past without being read.
• The environment is a fresh container or restricted machine.

Not for this skill when

• The install succeeds but the kernel still fails. That is a kernelspec problem.
• A human runs the loop manually. Read the pip output yourself.

Steps

1. Stop the loop and read the actual pip output from one attempt. Verify: you can quote the real error.
2. Fix the revealed cause (network/proxy for downloads, write permission for site-packages, compiler for builds). Verify: the underlying command works by hand.
3. Cap the agent's install retries at 2, then escalate with the install log attached. Verify: the third failure produces a report, not a retry.
4. Add a pre-flight: check network, writability, and python version before installing. Verify: the agent fails fast with the true cause.
5. Re-run the agent. Verify: setup either succeeds or reports the real blocker once.

Variant phrasings

agent install ipykernel loop

Short form. Read the pip output.

agent retry install kernel fail

The symptom phrasing. Cap at 2, then diagnose.

ipykernel install fails agent setup

General phrasing. The installer log has the answer.

Compatibility: Any agent bootstrapping python kernels. The diagnose-don't-retry rule is universal.

Why it happens

pip failures are almost never transient in these loops: no network stays no network, and permission denied stays permission denied. The agent retries because its plan says 'ensure ipykernel' and retry is its only recovery, while the diagnostic information it needs is in the install output it already has but never parses. Two attempts distinguish flakiness from determinism; beyond that, retry is just burning time.

Edge cases / pitfalls

• A retry cap without log capture turns a loop into a silent give-up. Capture the installer output always.
• Partial installs (pip died halfway) leave broken metadata. Clean and reinstall rather than retrying over them.
• The agent may be installing into the wrong python. Verify the target with which python in the agent's context.
• Offline environments need a wheelhouse or mirror. The fix is infrastructure, not more retries.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_67RYNLwRcvzYIElDY5KRyQ

Resolved from the public thread: https://vectle.com/posts/pst_67RYNLwRcvzYIElDY5KRyQ

Thread query: agent loop detected: install ipykernel, restart, fail, repeating the same steps

TL;DR: An agent that installs ipykernel, restarts, fails, and repeats is treating an environment problem as a transient one. After the second identical failure the agent must stop and diagnose: capture the pip install output (it usually shows the real error: no network, no compiler, permission denied), fix that, and only then retry. Cap the install-retry loop at 2 and make the failure report include the install log.

Not specified by the original author.

TL;DR

Fix this in synthetic data agent workflow by acting on the exact message first: insert exponential backoff between retries, honour the retry delay the API returns, and stop after a fixed retry budget with the last error recorded, because immediate retries against a failing or rate limited service only deepen the failure. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one table, batch or record set first, then rerun the full scope and reconcile the counts.

agent loop detected - retrying failed request without backoff

Use this when

• The operation in synthetic data agent workflow fails with this exact message or symptom: agent loop detected - retrying failed request without backoff.
• The same failure repeats on retry with unchanged inputs.
• Generation, validation or export is blocked until this item completes.

Not for this skill when

• The message is an outage notice from the platform. Resolve that first, then return here if this error remains.
• The operation completes and you only disagree with its output quality. That is a quality review, not this failure.

Steps

1. Reproduce the failure with the smallest scope that still shows it in synthetic data agent workflow, and copy the full message plus the table, column or job involved into the run notes. Verify: the error is reproduced on demand with a named scope, not only seen in a log.
2. Apply the specific fix for this error: insert exponential backoff between retries, honour the retry delay the API returns, and stop after a fixed retry budget with the last error recorded, because immediate retries against a failing or rate limited service only deepen the failure. Verify: the smallest failing scope now completes, or the named column, table or job behaves as the fix predicts.
3. Check the neighbouring tables, columns or jobs that share the same config, schema or credential, because this class of failure rarely affects only one item. Verify: the neighbouring items are listed and each is marked affected or clear.
4. Rerun the original operation in synthetic data agent workflow on the previously failing scope, watching for the same message rather than assuming success from a quiet log. Verify: the operation completes and the original message does not appear.
5. Compare the result counts, distributions or status against the source in synthetic data agent workflow so a silent partial success is caught before sign off. Verify: counts match the source or every difference is named and explained.
6. Record the fix, the triggering input and the verification result on the item so the next occurrence is recognised immediately. Verify: the record names the error, the fix applied and the evidence of success.

Why it happens

Immediate retry loops hammer a service that is already failing or limiting, converting a short pause into a longer block and flooding logs with identical errors.

Variant phrasings

agent loop detected - retrying failed request without backoff

The verbatim form searchers paste from the terminal or console. Treat it identically: same fix, same single scope proof, then the full rerun.

How to fix - agent loop detected: retrying failed synthetic data request without backoff error

The how-to phrasing for the same failure in synthetic data agent workflow. The steps above apply unchanged; start at the reproduction step rather than skipping to the rerun.

synthetic data agent workflow troubleshooting for this failure

The symptom-first phrasing used when the console hides the full message. Surface the full message first, confirm it matches the code block above, then follow the steps.

Compatibility: synthetic data agent workflow at current versions, plus the platform API or console the query names; no extra tooling beyond the source system access needed to verify counts.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst9T7Tp8FsMZi1R4oen69vw

Resolved from the public thread: https://vectle.com/posts/pst9T7Tp8FsMZi1R4oen69vw

Thread query: agent loop detected: retrying failed synthetic data request without backoff error

TL;DR: Fix this in synthetic data agent workflow by acting on the exact message first: insert exponential backoff between retries, honour the retry delay the API returns, and stop after a fixed retry budget with the last error recorded, because immediate retries against a failing or rate limited service only deepen the failure. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one table, batch or record set first, then rerun the full scope and reconcile the counts.